scrape_amazon_product

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full responsibility for disclosing behavioral traits. It does mention the tool scrapes a 'live' page, implying a network dependency and real-time data, and notes a derived sales/revenue estimate, which adds some context. However, it does not disclose potential failure modes (e.g., invalid ASIN, page not found, changes in Amazon's layout), rate limits, costs, or execution time. For a web scraping tool, this is a significant gap in behavioral transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, efficient and dense with useful information. The core action (scrape by ASIN) is front-loaded, followed by an enumeration of the data points. It is a bit long with many comma-separated items, but every word adds value. It avoids fluff and clearly prioritizes the essential information. Slightly over-packed, but acceptable.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity and the fact that an output schema exists (which defines the return structure), the description covers the main purpose and data points well. However, it misses important context: it does not explain the purpose of the 'render' parameter, does not mention prerequisites (e.g., valid Amazon India ASIN, region restrictions), and does not clarify how the sales/revenue estimate is derived. While the output schema handles return types, these gaps reduce completeness for an agent deciding to use the tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, meaning the description must explain parameters. It clearly explains that 'asin' is the Amazon product identifier and scopes it to India, but it completely omits the 'render' parameter. The description does not mention what 'render' does (e.g., whether it enables JavaScript rendering for dynamic content). This leaves one of two parameters undefined, requiring the agent to guess or rely on the parameter name alone.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states a specific verb ('scrape'), a specific resource ('one live Amazon India product page by ASIN'), and enumerates the exact data points returned (title, brand, price, rating, etc.). It distinguishes itself from sibling tools like scrape_amazon_search (which scrapes search results) and scrape_listing_details by focusing on a single product page and adding a sales/revenue estimate. No ambiguity remains about what this tool does.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage: it is for scraping a single product page by ASIN, useful when detailed product data is needed for one specific product. However, it does not explicitly state when to choose this tool over alternatives like research_product or analyze_product_demand, nor does it mention when not to use it. There is no exclusions or comparison to siblings, so an agent must infer the appropriate context from the tool name and description.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.